Performance Analysis of Machine Learning Algorithms and Feature Extraction Methods for Sentiment Analysis

Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction methods, such as SNP calling and counting nucleotide k-mers have been proposed for presenting DNA sequences to the model. However, there are trade-offs between interpretability, computational complexity and accuracy for different feature extraction methods. In this study, we have proposed a new feature extraction method, counting amino acid k-mers or oligopeptides, which provides easier model interpretation compared to counting nucleotide k-mers and reaches the same or even better accuracy in comparison with different methods. Additionally, we have trained machine learning algorithms using different feature extraction methods and compared the results in terms of accuracy, model interpretability and computational complexity. We have built a new feature selection pipeline for extraction of important features so that new AMR determinants can be discovered by analyzing these features. This pipeline allows the construction of models that only use a small number of features and can predict resistance accurately.

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URLCam: Toolkit for malicious URL analysis and modeling

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189874 ◽

2021 ◽

pp. 1-15

Author(s):

Mohammed Ayub ◽

El-Sayed M. El-Alfy

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Feature Selection ◽

State Of The Art ◽

Learning Algorithms ◽

Extraction Methods ◽

Machine Learning Algorithms ◽

The Other ◽

Imbalanced Learning ◽

Almost All

Web technology has become an indispensable part in human’s life for almost all activities. On the other hand, the trend of cyberattacks is on the rise in today’s modern Web-driven world. Therefore, effective countermeasures for the analysis and detection of malicious websites is crucial to combat the rising threats to the cyber world security. In this paper, we systematically reviewed the state-of-the-art techniques and identified a total of about 230 features of malicious websites, which are classified as internal and external features. Moreover, we developed a toolkit for the analysis and modeling of malicious websites. The toolkit has implemented several types of feature extraction methods and machine learning algorithms, which can be used to analyze and compare different approaches to detect malicious URLs. Moreover, the toolkit incorporates several other options such as feature selection and imbalanced learning with flexibility to be extended to include more functionality and generalization capabilities. Moreover, some use cases are demonstrated for different datasets.

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A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts

Sensors ◽

10.3390/s21175779 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5779

Author(s):

Runqiong Wang ◽

Qinghua Song ◽

Zhanqiang Liu ◽

Haifeng Ma ◽

Munish Kumar Gupta ◽

...

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Learning Algorithms ◽

Bending Moment ◽

Extraction Methods ◽

Machine Learning Algorithms ◽

Chatter Detection ◽

Unsupervised Machine Learning ◽

Milling Chatter ◽

Better Than

Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for the accuracy disadvantage of machine learning algorithms to some extent if the extracted features are highly correlated with the milling condition. However, the classification accuracy of the current feature extraction methods is not satisfactory, and a combination of multiple features is required to identify the chatter. This limits the development of unsupervised machine learning algorithms for chattering detection, which further affects the application in practical processing. In this paper, the fractal feature of the signal is extracted by structure function method (SFM) for the first time, which solves the problem that the features are easily affected by process parameters. Milling chatter is identified based on k-means algorithm, which avoids the complex process of training model, and the judgment method of milling chatter is also discussed. The proposed method can achieve 94.4% identification accuracy by using only one single signal feature, which is better than other feature extraction methods, and even better than some supervised machine learning algorithms. Moreover, experiments show that chatter will affect the distribution of cutting bending moment, and it is not reliable to monitor tool wear through the polar plot of the bending moment. This provides a theoretical basis for the application of unsupervised machine learning algorithms in chatter detection.

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